{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iris DATASET 实践"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "from pandas.plotting import scatter_matrix\n",
    "from matplotlib import pyplot\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data\n",
    "filename = '/Users/elvis/Downloads/iris.data.csv'\n",
    "names=['separ-length','separ-width','petal-length','petal-width','class']\n",
    "dataset = read_csv(filename,names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>separ-length</th>\n",
       "      <th>separ-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   separ-length  separ-width  petal-length  petal-width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
       "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
       "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
       "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
       "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
       "9           4.9          3.1           1.5          0.1  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>separ-length</th>\n",
       "      <th>separ-width</th>\n",
       "      <th>petal-length</th>\n",
       "      <th>petal-width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       separ-length  separ-width  petal-length  petal-width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分布情况\n",
    "dataset.groupby('class').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "separ-length       AxesSubplot(0.125,0.125;0.168478x0.755)\n",
       "separ-width     AxesSubplot(0.327174,0.125;0.168478x0.755)\n",
       "petal-length    AxesSubplot(0.529348,0.125;0.168478x0.755)\n",
       "petal-width     AxesSubplot(0.731522,0.125;0.168478x0.755)\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱线图\n",
    "dataset.plot(kind='box',subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  layout[ax.rowNum, ax.colNum] = ax.get_visible()\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: \n",
      "The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.\n",
      "  if not layout[ax.rowNum + 1, ax.colNum]:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1291d1760>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x128efdb50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x128166fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x129022490>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#直方图\n",
    "dataset.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x12863c1c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1282843d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x128274760>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x128554b80>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x128862fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1290df3a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1290df490>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x128a33940>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x12856d1c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1282ee610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x128233a60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x12912aeb0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x129184340>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x129182c10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1290c63d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x12906ab50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点矩阵图\n",
    "scatter_matrix(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分离数据集\n",
    "array = dataset.values\n",
    "x = array[:,0:4]\n",
    "y = array[:,4]\n",
    "validation_size = 0.2\n",
    "seed =7\n",
    "x_train,x_validation,y_train,y_validation = train_test_split(x,y,test_size=validation_size,random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#算法审查\n",
    "models={}\n",
    "models['LR'] = LogisticRegression() #逻辑线性\n",
    "models['LDA'] = LinearDiscriminantAnalysis() #线性判别分析\n",
    "models['KNN']= KNeighborsClassifier() #K最近邻\n",
    "models['CART'] = DecisionTreeClassifier() #卡特\n",
    "models['NB'] = GaussianNB()#朴素贝叶斯\n",
    "models['SVM'] = SVC()#支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:292: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:938: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:938: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:938: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR:0.983333(0.033333)\n",
      "LDA:0.975000(0.038188)\n",
      "KNN:0.983333(0.033333)\n",
      "CART:0.983333(0.033333)\n",
      "NB:0.975000(0.053359)\n",
      "SVM:0.983333(0.033333)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:292: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:292: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:292: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:292: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_split.py:292: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#评估算法\n",
    "results = []\n",
    "for key in models:\n",
    "    kfold = KFold(n_splits=10,random_state=seed)#10折\n",
    "    cv_results = cross_val_score(models[key],x_train,y_train,cv=kfold,scoring='accuracy')\n",
    "    results.append(cv_results)\n",
    "    print('%s:%f(%f)' % (key,cv_results.mean(),cv_results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#箱线图比较算法\n",
    "fig = pyplot.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.boxplot(results)\n",
    "ax.set_xticklabels(models.keys())\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9\n",
      "[[ 7  0  0]\n",
      " [ 0 10  2]\n",
      " [ 0  1 10]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       0.91      0.83      0.87        12\n",
      " Iris-virginica       0.83      0.91      0.87        11\n",
      "\n",
      "       accuracy                           0.90        30\n",
      "      macro avg       0.91      0.91      0.91        30\n",
      "   weighted avg       0.90      0.90      0.90        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#使用评估数据集评估算法\n",
    "svm = DecisionTreeClassifier() #SVC()\n",
    "svm.fit(X=x_train,y=y_train)\n",
    "predictions = svm.predict(x_validation)\n",
    "print(accuracy_score(y_validation,predictions))\n",
    "print(confusion_matrix(y_validation,predictions))\n",
    "print(classification_report(y_validation,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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